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Hyperspectral Image Classification

Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )

Papers

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Showing 131–140 of 286 papers

TitleStatusHype
Is Pretraining Necessary for Hyperspectral Image Classification?—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme—0
Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
A Supervised Segmentation Network for Hyperspectral Image Classification—0
1D-Convolutional Capsule Network for Hyperspectral Image Classification—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Deep Learning for Hyperspectral Image Classification: An Overview—0
Hyperspectral image classification using spectral-spatial LSTMs—0
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